Developing a digital twin for testing multi-agent systems in advanced air mobility: a case study of Cranfield University and airport

Date published

2023-11-10

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Publisher

IEEE

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Type

Conference paper

ISSN

2155-7195

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Citation

Conrad C, Delezenne Q, Mukherjee A, et al., (2023) Developing a digital twin for testing multi-agent systems in advanced air mobility: a case study of Cranfield University and airport. In: IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC) 2023, 1-5 October 2023, Barcelona, Spain

Abstract

Emerging unmanned aircraft system (UAS) and advanced air mobility (AAM) ecosystems rely on the development, certification and deployment of new and potentially intelligent technologies and algorithms. To promote a more efficient development life cycle, this work presents a digital twin architecture and environment to support the rapid prototyping and testing of multi-agent solutions for UAS and AAM applications. It leverages the capabilities of Microsoft AirSim and Cesium as plugins within the Unreal Engine 3D visualisation tool, and consolidates the digital environment with a flexible and scalable Python-based architecture. Moreover, the architecture supports hardware-in-the-loop (HIL) and mixed-reality features for enhanced testing capabilities. The system is comprehensively documented and demonstrated through a series of use cases, deployed within a custom digital environment, comprising both indoor and outdoor areas at Cranfield University and Airport. These include collaborative surveillance, UTM flight authorisation and UTM conformance monitoring experiments, that showcase the modularity, scalability and functionality of the proposed architecture. All 3D models and experimental observations are critically evaluated and shown to exhibit promising results. This thereby represents a critical step forward in the development of a robust digital twin for UAS and AAM applications.

Description

Software Description

Software Language

Github

Keywords

Advanced air mobility, AirSim, digital twin, mixed-reality, multi-agent, UAS

DOI

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Attribution 4.0 International

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Funder/s

UKRI: 10024815